Self-supervised representation learning (SSL) on biomedical networks provides
new opportunities for drug discovery. However, how to effectively combine
multiple SSL models is still challenging and has been rarely explored.
Therefore, we propose multi-task joint strategies of self-supervised
representation learning on biomedical networks for drug discovery, named
MSSL2drug. We design six basic SSL tasks inspired by various modality features
including structures, semantics, and attributes in heterogeneous biomedical
networks. Importantly, fifteen combinations of multiple tasks are evaluated by
a graph attention-based multi-task adversarial learning framework in two drug
discovery scenarios. The results suggest two important findings. (1)
Combinations of multimodal tasks achieve the best performance compared to other
multi-task joint models. (2) The local-global combination models yield higher
performance than random two-task combinations when there are the same size of
modalities. Therefore, we conjecture that the multimodal and local-global
combination strategies can be treated as the guideline of multi-task SSL for
drug discovery.

本文中提出了一种名为 MSSL2drug 的多任务自我监督表征学习的生物医学网络方法，使用基于图注意力机制的对抗学习框架， 评估了包括结构，语义和属性在内的多种模态特征的六种基本任务，其中的多模态、局部 - 全局组合策略可以被看作是多任务自我监督表征学习用于药物研发的指南。